Automated Knowledge Base Management with Notion, GPT, and Vector Search

This workflow automates the process of updating and managing a knowledge base by integrating Notion pages, generating embeddings with OpenAI, storing them in a vector database (Supabase), and enabling intelligent Q&A interactions. It can periodically fetch updated pages from Notion, process their content by splitting into chunks, generate semantic embeddings, and store these in a vector store for fast retrieval. A chatbot trigger allows users to ask questions based on the stored knowledge, leveraging GPT-4 for accurate responses. The workflow also includes cleaning up old embeddings to keep the database current, making it ideal for maintaining a dynamic, searchable knowledge repository for applications like customer support, internal documentation, or research assistant tools.